Generative AI Image Tools for Creative Work: Social and Ethical Perspectives in Japan from Computer Science Graduate Students and Experts
Bibliographic record
Abstract
The growing interest to incorporate generative artificial intelligence (GenAI) image tools into creative workflows has raised concerns about the social and ethical implications it may have on Japan’s creative industries. This exploratory study is the first to discuss what oversights may emerge on such issues from prospective Japanese generative AI researchers- computer science (CS) graduate students studying in Japan. From June 2023 to August 2023, nine CS graduate students studying in Tokyo were interviewed to understand how CS graduate students in Japan discuss GenAI image tools’ 1) technical aspects, 2) social and ethical aspects, and 3) cultures in AI research, as well as three experts to investigate the 4) legal, social, and cultural impacts of using GenAI image tools for creative work in Japan. The results indicate that CS graduate students do discuss various ethical and social aspects with GenAI image tools, but many neglected to see how widespread industry usage in Japan has the ability to further marginalize artists in creative workplaces and jeopardize critical aspects of workplace pedagogy in creative industries. This study provides insight into the mindsets of prospective GenAI researchers in Japan and indicates areas of future work that can better prepare them as future knowledge holders and innovators in the field. AI researchers from Canada, Japan, and around the world are encouraged to adopt participatory AI design practices to involve stakeholders throughout the planning, design, and evaluation processes of GenAI image research so they respond to the needs, values, and concerns of artists and creative professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".